Github user yanboliang commented on the pull request:
https://github.com/apache/spark/pull/4911#issuecomment-77572142
@mengxr Yes, it make sense. After look through the code, I found we have
two alternatives:
1, Implement a new PythonMLLibAPI looks like this
def newGeneralizedLinearModel(
modelClass: String,
weights: Vector,
intercept: Double): GeneralizedLinearModel {
}
And at pyspark we could get corresponding java model by this api and call
save/load of java model which can make the model saved in Scala loadable in
Python and vice versa.
2, Implement the save/load operation in Python independently which do the
same thing as in Scala.
I prefer the first one. Because in the second scenario, if we update the
save/load in Scala we need to keep the save/load function in Python have the
same behavior which may leads to inconsistent.
Any comments and suggestions?
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